# Predicting intrahepatic recurrence of colorectal cancer liver metastases after curative hepatectomy using a machine learning model with data integration of ultrasound radiomics and clinicopathological parameters

## TL;DR

A machine learning model combining ultrasound imaging data and clinical factors improves the prediction of cancer recurrence in liver metastases patients after surgery.

## Contribution

A novel machine learning model integrating ultrasound radiomics and clinicopathological data for predicting intrahepatic recurrence in CRLM patients.

## Key findings

- The cRadiomics model achieved AUC values of 0.811 and 0.784 in main and external cohorts, outperforming clinical and radiomics-only models.
- Six clinical parameters and seven radiomics features were identified as strong predictors of intrahepatic recurrence.
- The model shows potential to improve clinical decision-making and personalized follow-up for CRLM patients.

## Abstract

To develop and validate a machine learning model integrating ultrasound radiomics and clinicopathological parameters to predict intrahepatic recurrence in colorectal cancer liver metastases (CRLM) patients after curative hepatectomy.

This retrospective study enrolled 278 eligible CRLM patients (age, 55 ± 12 years; male, 188) from two centers, including a main cohort (n = 224, July 2010–February 2021) and an external cohort (n = 54, February 2015–October 2020). Patients were stratified by recurrence status during a 2-year follow-up. Preoperative ultrasound images and clinicopathological parameters were collected. Radiomics features were extracted from liver metastases, peri-tumor areas, and disease-free liver parenchyma. Using least absolute shrinkage and selection operator (LASSO) analysis and support vector machine algorithms, three predictive models were developed: clinical, radiomics, and clinical-radiomics combined (cRadiomics) models. Model performance was assessed using five-fold cross-validation (main cohort) and external validation (external cohort), with metrics including receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), accuracy, sensitivity, and specificity.

Six clinical parameters (pathological lymph node positivity, synchronous liver metastases, bilobar liver metastases, preoperative chemotherapy, use of targeted drugs, and preoperative CA19-9 > 200 U/mL) and seven radiomics features were identified as strong predictors. The cRadiomics model achieved AUC values of 0.811 (95% CI: 0.755–0.861) and 0.784 (95% CI: 0.644–0.880) during testing on the main cohort and external cohort data, respectively, significantly outperforming both radiomics (AUC 0.744 and 0.724; p < 0.01) and clinical models (AUC 0.706 and 0.696; p < 0.05).

The cRadiomics model, integrating ultrasound radiomics and clinicopathological parameters, improved the prediction of intrahepatic recurrence within two years for colorectal liver metastases after curative hepatectomy.

The cRadiomics model, with enhanced accuracy in predicting intrahepatic recurrence of colorectal liver metastases after curative hepatectomy, holds great potential to improve clinical decision-making and enable personalized management and risk-adapted follow-up for colorectal cancer liver metastases (CRLM) patients.

The cRadiomics model can predict intrahepatic recurrence of colorectal cancer liver metastases (CRLM) after curative hepatectomy.Machine learning with data integration of ultrasound radiomics and clinicopathological parameters enables better prediction of intrahepatic recurrence for CRLM after curative hepatectomy.The developed model holds great potential to improve clinical decision-making and personalized management for CRLM patients.

The cRadiomics model can predict intrahepatic recurrence of colorectal cancer liver metastases (CRLM) after curative hepatectomy.

Machine learning with data integration of ultrasound radiomics and clinicopathological parameters enables better prediction of intrahepatic recurrence for CRLM after curative hepatectomy.

The developed model holds great potential to improve clinical decision-making and personalized management for CRLM patients.